This study focuses on parameter optimization and detection signal comparison of planar coil particle detection sensors, and conducts in-depth analysis of different core materials. Through finite element simulation analysis and experimental verification, the influence of coil turns, diameter, thickness and other parameters on magnetic field distribution and particle detection signals was systematically explored, and the sensor chip structure design was optimized. The study found that a planar coil with 60 turns and an inner diameter of 1.2 mm performed best. At the same time, the enhancement effect of different magnetic core materials (including iron core, silicon steel sheet and perm alloy) on the magnetic field and detection signal was investigated. It was found that the perm alloy magnetic core can significantly improve the sensor performance. Copper particles have effective detection capabilities. This research provides theoretical and experimental basis for optimizing the detection sensor of metal particle contaminants in oil, and has important academic value and application prospects.

Abstract

Mental health outcomes are profoundly shaped by dynamic interactions between individuals and their spatiotemporal environments. However, conventional stationary methods often overlook real-time, context-specific exposures during mobility, leading to the uncertain geographic context problem (UGCoP). This study employs geographic ecological momentary assessment (GEMA)—an innovative framework integrating ecological momentary assessment, GPS tracking, and geographic information systems (GIS)—to capture high-resolution data on individuals’ momentary environmental exposures and psychological states. This methodology enables granular analysis of environmental determinants of mental health, emphasizing dynamic mobility patterns and their spatiotemporal-activity contexts.

The results reveal that relationships between environmental determinants and mental health outcomes exhibit significant spatiotemporal and activity-dependent heterogeneity. For example, compared to residential neighborhood-based assessments, exposure to green spaces during mobility demonstrated a stronger negative association with psychological stress, with distinct time-threshold effects. Similarly, noise exposure influenced stress through nonlinear, context-dependent patterns across spatial and activity settings.

These findings advance precision public health by advocating spatiotemporally targeted interventions. Policymakers should prioritize context-aware urban design, including dynamic green infrastructure placement and exposure-minimizing routing algorithms, to address mental health disparities. GEMA’s ability to synchronize real-world environmental dynamics with psychological responses holds transformative potential for nursing practice and community health strategies, enabling personalized, place-based care frameworks.

Abstract

Improving energy management is essential for sustainability since building energy use is rising. This study is a trailblazer in using multi-criteria decision-making (MCDM) methodologies for the real-time operational optimization of building energy systems. Data collection and pre-processing, feature extraction, feature selection, classification, trust authentication, encryption, and decryption are among the techniques used in this approach. Pre-processing procedures for the raw data include feature encoding, dimension reduction, and normalization approaches. The Hybrid Grey Level Cooccurrence Matrix Fast Fourier Transform (HGLCM-FFT) method is used for feature extraction. Filter-based methods are used for feature selection, including IG, CS, symmetric uncertainty, and gain ratio. The Hierarchical Gradient Boosted Isolation Forest (HGB-IF) technique is used for the classification. Distributed Adaptive Trust-Based Authentication (DAT-BA), a security architecture in distributed cloud environments, uses trust authentication. The Particle Swarm Optimized Symmetrical Blowfish (PSOSB) method is used for encryption and decryption. The suggested work is implemented using OS Python – 3.9.6; the performance of the proposed model is Attack Detection Rate, False alarm rate, True positive rate, Network usage, CPU usage, Encryption time, encryption time and Throughput.

Abstract

Life expectancy has increased significantly due to declining mortality rates, especially a mong adults and older individuals. However, this rapid aging of the population has also led to a rise in age-related diseases, putting more people at risk of chronic illness and disability. Maintaining health, resilience, and independence throughout life has thus become a key research priority, vital both for individual well-being and the sustainability of healthcare systems. Studies on longevity populations provide insights into factors that may beneficially alter the epigenome over a lifetime, promoting health and longevity. Longevity is seen as a major achievement of modern society, reflecting not only an extended lifespan but also a prolonged period of health. Certain regions, known as Longevity Blue Zones (LBZs)—such as Okinawa (Japan), the Nicoya Peninsula (Costa Rica), Loma Linda (California), Ikaria (Greece), and Ogliastra (Sardinia)—are celebrated for their high percentage of long-lived residents who often avoid age-related disabilities. Longevity is not universally distributed, so identifying LBZ-like areas worldwide is crucial to understanding additional factors that may contribute to a long, healthy life. This narrative review aims to explore the commonalities between the Cilento region in Italy and recognized LBZs, examining environmental, dietary, and lifestyle factors.We conducted a search for articles published from 2004 to the present, limited to English, German, and Italian studies in PubMed/Medline, Scopus, and Google Scholar. Through collaborative consensus, 18 final references were selected. A descriptive comparative approach was used to evaluate the similarities between Cilento and LBZs.Cilento and LBZs share several characteristics, including hilly terrain, moderate altitudes (between 355 and 600 meters), and mild climates (average temperatures of 17.4 to 23.5°C). Both regions exhibit traditional professions such as farming and animal husbandry, a largely Mediterranean or plant-based diet rich in legumes, vegetables, and olive oil, and strong social cohesion with deep family, religious, and community ties. These traits promote resilience, optimism, and mental well-being, which are essential for healthy aging.Given its similarities, Cilento may represent an as-yet-unrecognized LBZ. Insights from this region could be applied more broadly to help prevent chronic diseases and slow aging. Studying Cilento’s centenarians may also offer new parameters for understanding why certain regions enjoy extended life expectancy.